title: Tobacco-related cancer mortality: projections for different geographical regions in Switzerland.
abstract: BACKGROUND
  Switzerland is divided into 26 cantons of variable population size and cultural characteristics. Although a federal law to protect against passive smoking and a national tobacco control programme exist, details of tobacco-related policies are canton-specific. This study aimed to project gender-specific tobacco-related cancer mortality in Switzerland at different geographical levels for the periods 2009-2013 and 2014-2018.
  
  
  METHODS
  In this analysis, data on Swiss tobacco-related cancer mortality from 1984 until 2008 were used. Bayesian age-period-cohort models were formulated to assess past trends of gender-specific tobacco-related cancer mortality and to project them up to 2018 at cantonal and language region levels. Furthermore, estimates are provided on a national scale by age categories of 50-69 and ≥70 years.
  
  
  RESULTS
  Model-based estimates at cantonal level identified regions with low and high tobacco-related cancer mortality rates for the observed and projected periods. Our analysis based on language regions showed the lowest mortality in the German-speaking part. Projections at national level, between younger (age 50-69) and older (age ≥70) males, indicated an ongoing decreasing trend for males but an upward trend for females. The gap in tobacco-related cancer mortality rates between younger and older males seems to be shrinking. In females, a stronger rise was obtained for the younger age group.
  
  
  CONCLUSIONS
  Our findings indicate region-, sex- and age-related differences in tobacco-related cancer mortality in Switzerland and this could be useful for healthcare planning and for evaluating the impact of canton-specific tobacco-related policies and interventions.
authors.affiliation: Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Basel, Switzerland.
authors.forename: Verena
authors.initials: V
authors.lastname: Jürgens
authors.forename: Silvia
authors.initials: S
authors.lastname: Ess
authors.forename: Harish C
authors.initials: HC
authors.lastname: Phuleria
authors.forename: Martin
authors.initials: M
authors.lastname: Früh
authors.forename: Matthias
authors.initials: M
authors.lastname: Schwenkglenks
authors.forename: Harald
authors.initials: H
authors.lastname: Frick
authors.forename: Thomas
authors.initials: T
authors.lastname: Cerny
authors.forename: Penelope
authors.initials: P
authors.lastname: Vounatsou
mesh_terms: D000368:Aged; D001499:Bayes Theorem; D005260:Female; D005544:Forecasting; D006801:Humans; D008297:Male; D008875:Middle Aged; D009026:Mortality / Q000639:trends*; D009369:Neoplasms / Q000209:etiology / Q000401:mortality*; D012907:Smoking / Q000009:adverse effects / Q000401:mortality*; D013557:Switzerland / Q000453:epidemiology